OFA-Diffusion Compression trains diffusion models once to yield multiple size-specific compressed subnetworks via restricted candidate spaces, importance-based channel allocation, and reweighting.
On architectural compression of text-to-image diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
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2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
SDXL improves upon prior Stable Diffusion versions through a larger UNet backbone, dual text encoders, novel conditioning, and a refinement model, producing higher-fidelity images competitive with black-box state-of-the-art generators.
citing papers explorer
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2ndMatch: Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching
2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.